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| author | Nikhil Raj <nikhil.raj@arm.com> | 2022-03-08 20:01:38 +0000 |
|---|---|---|
| committer | Nikhil Raj <nikhil.raj@arm.com> | 2022-03-09 12:26:14 +0000 |
| commit | f4019872c1134c6fcc1d6993e5746f55c1e79208 (patch) | |
| tree | b07ea8bdd70d696adfa3814344e210ea67be1e8c /22.02/_pooling3d_test_impl_8cpp_source.xhtml | |
| parent | 0d75c02b21b919b81035205f3914ee273b93b30c (diff) | |
| download | armnn-f4019872c1134c6fcc1d6993e5746f55c1e79208.tar.gz | |
IVGCVSW-6819 Fix the directory structure and broken link to latest docu
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I05b559d15faf92c76ff536719693b361316be4f3
Diffstat (limited to '22.02/_pooling3d_test_impl_8cpp_source.xhtml')
| -rw-r--r-- | 22.02/_pooling3d_test_impl_8cpp_source.xhtml | 226 |
1 files changed, 226 insertions, 0 deletions
diff --git a/22.02/_pooling3d_test_impl_8cpp_source.xhtml b/22.02/_pooling3d_test_impl_8cpp_source.xhtml new file mode 100644 index 0000000000..780a72d85e --- /dev/null +++ b/22.02/_pooling3d_test_impl_8cpp_source.xhtml @@ -0,0 +1,226 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/backends/backendsCommon/test/layerTests/Pooling3dTestImpl.cpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">22.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_pooling3d_test_impl_8cpp_source.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">Pooling3dTestImpl.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_pooling3d_test_impl_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_pooling3d_test_impl_8hpp.xhtml">Pooling3dTestImpl.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">armnnUtils/QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_tensor_utils_8hpp.xhtml">armnnUtils/TensorUtils.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_numeric_cast_8hpp.xhtml">armnn/utility/NumericCast.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <<a class="code" href="_backend_helper_8hpp.xhtml">armnn/BackendHelper.hpp</a>></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <<a class="code" href="src_2backends_2backends_common_2_workload_info_8hpp.xhtml">backendsCommon/WorkloadInfo.hpp</a>></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml">armnnTestUtils/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_test_utils_2_workload_test_utils_8hpp.xhtml">armnnTestUtils/WorkloadTestUtils.hpp</a>></span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_test_utils_2_tensor_helpers_8hpp.xhtml">armnnTestUtils/TensorHelpers.hpp</a>></span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> {</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> SimplePooling3dTestImpl(</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor,</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  int32_t qOffset,</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keyword">const</span> std::vector<T>& input,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keyword">const</span> std::vector<T>& outputExpected,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& inputShape,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& outputShape)</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a>;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> dimensionIndices = dataLayout;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">auto</span> heightIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">auto</span> widthIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">auto</span> depthIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a45687cf74c4aeeacc6605a1f7ffe582a">GetDepthIndex</a>();</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">auto</span> channelsIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputDepth = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[depthIndex]);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[heightIndex]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[widthIndex]);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[channelsIndex]);</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputShape[0]);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDepth = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputShape[depthIndex]);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputShape[heightIndex]);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputShape[widthIndex]);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputShape[channelsIndex]);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = <a class="code" href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputShape[0]);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  inputBatchSize, inputChannels, inputDepth, inputHeight, inputWidth, dataLayout, ArmnnType);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  outputBatchSize, outputChannels, outputDepth, outputHeight, outputWidth, dataLayout, ArmnnType);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">if</span> (armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  inputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> result(outputTensorInfo);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::vector<T> actualOutput(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="structarmnn_1_1_pooling3d_queue_descriptor.xhtml">armnn::Pooling3dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = descriptor;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// Don't execute if Pooling is not supported, as an exception will be raised.</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a> backend = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>();</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  std::string reasonIfUnsupported;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="classarmnn_1_1_layer_support_handle.xhtml">armnn::LayerSupportHandle</a> handle = <a class="code" href="namespacearmnn.xhtml#a6d9d90e2ace1cd49789835b0073196a3">armnn::GetILayerSupportByBackendId</a>(backend);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  result.m_Supported = handle.<a class="code" href="classarmnn_1_1_layer_support_handle.xhtml#ada2164a21874acb3647cc9224b31a80a">IsPooling3dSupported</a>(inputTensorInfo,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  outputTensorInfo,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  reasonIfUnsupported);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">if</span> (!result.m_Supported)</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  }</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> </div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a13060ebb89d2c21a7a5d897e99cccf72">CreateWorkload</a>(<a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c">armnn::LayerType::Pooling3d</a>,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  queueDescriptor,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  workloadInfo);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  inputHandle->Allocate();</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  outputHandle->Allocate();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> </div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), input.data());</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  workload->Execute();</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ab5dfed8358e500ed523d78090ec78e88">CopyDataFromITensorHandle</a>(actualOutput.data(), outputHandle.get());</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  result.m_ActualData = actualOutput;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  result.m_ExpectedData = outputExpected;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="comment">//</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="comment">// Tests max pooling with the following parameters:</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment">//</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="comment">// Pooling size: 2x2x2</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="comment">// Stride: (1,1,1)</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="comment">// input size: 3x3x3</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="comment">// channels: 2</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="comment">// batch size: 2</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <span class="comment">//</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon(</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 2;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 2;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 2;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 1;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> = 0;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 3;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputDepth = 3;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth =</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  (inputWidth + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>) /</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight =</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  (inputHeight + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>) /</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDepth =</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  (inputDepth + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> + descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> - descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a>) /</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a>;</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 2;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, channels, inputDepth, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ batchSize, channels, outputDepth, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  std::vector<float> singleChannelData({</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  1.0f, 1.0f, 1.0f,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  });</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="comment">// Constructs input data.</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  std::vector<float> inputData;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">auto</span> negator = [](<span class="keywordtype">float</span> f) { <span class="keywordflow">return</span> -f; };</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="comment">// First image (two channels where the second channel is the negative of the first one).</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end());</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="comment">// Second image (same as first image).</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end());</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">auto</span> input = QuantizedVector<T>(inputData, qScale, qOffset);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="comment">// These were calculated manually.</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  std::vector<T> outputExpected = QuantizedVector<T>(</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  1.0f, 1.0f,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  1.0f, 1.0f,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  1.0f, 1.0f,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  1.0f, 1.0f,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  1.0f, 1.0f,</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  1.0f, 1.0f,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  1.0f, 1.0f,</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  1.0f, 1.0f,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  -1.0f, -1.0f,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  },</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  qScale, qOffset);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> }</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> SimpleMaxPooling3dTestCommon(</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a>,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 2;</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 2;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 1, 4, 4, 4, dataLayout, ArmnnType);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 1, 2, 2, 2, dataLayout, ArmnnType);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  {</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  }</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  std::vector<T> inputData(</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  QuantizedVector<T>({</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  1.0f, 2.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  3.0f, 4.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  9.0f, 10.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  11.0f, 12.0f, 15.0f, 16.0f,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  17.0f, 18.0f, 21.0f, 22.0f,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  19.0f, 20.0f, 23.0f, 24.0f,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  25.0f, 26.0f, 29.0f, 30.0f,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  27.0f, 28.0f, 31.0f, 32.0f,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  33.0f, 34.0f, 37.0f, 38.0f,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  35.0f, 36.0f, 39.0f, 40.0f,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  41.0f, 42.0f, 45.0f, 46.0f,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  43.0f, 44.0f, 47.0f, 48.0f,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  49.0f, 50.0f, 53.0f, 54.0f,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  51.0f, 52.0f, 55.0f, 56.0f,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  57.0f, 58.0f, 61.0f, 62.0f,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  59.0f, 60.0f, 63.0f, 64.0f,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  },</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  qScale, qOffset));</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  std::vector<T> outputData(</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  QuantizedVector<T>({</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  20.0f, 24.0f,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  28.0f, 32.0f,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  52.0f, 56.0f,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  60.0f, 64.0f,</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  },</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  qScale, qOffset));</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCDHWToNDHWC = { 0, 4, 1, 2, 3 };</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a>)</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCDHWToNDHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  inputData = tmp;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  std::vector<T> tmp1(outputData.size());</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCDHWToNDHWC, outputData.data(), tmp1.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  outputData = tmp1;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  }</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  inputData, outputData, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> }</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> IgnorePaddingSimpleMaxPooling3dTestCommon(</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 2;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 2;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> = 1;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> = 1;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  }</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keyword">auto</span> input = QuantizedVector<T>(</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  {</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> </div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  -1.0f, -2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  1.0f, 2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  },</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  qScale, qOffset);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">auto</span> outputExpected = QuantizedVector<T>(</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  {</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  -1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  1.0f, 2.0f, -4.0f,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  -1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  1.0f, 2.0f, -4.0f,</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> </div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  -1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  1.0f, 2.0f, -4.0f,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  },</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  qScale, qOffset);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  input, outputExpected, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> }</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> SimpleAveragePooling3dTestCommon(</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a>,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 2;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 2;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 1, 4, 4, 4, dataLayout, ArmnnType);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 1, 2, 2, 2, dataLayout, ArmnnType);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> </div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  {</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  }</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  std::vector<T> inputData(</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  QuantizedVector<T>({</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  1.0f, 2.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  3.0f, 4.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  9.0f, 10.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  11.0f, 12.0f, 15.0f, 16.0f,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> </div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  17.0f, 18.0f, 21.0f, 22.0f,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  19.0f, 20.0f, 23.0f, 24.0f,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  25.0f, 26.0f, 29.0f, 30.0f,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  27.0f, 28.0f, 31.0f, 32.0f,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> </div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  33.0f, 34.0f, 37.0f, 38.0f,</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  35.0f, 36.0f, 39.0f, 40.0f,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  41.0f, 42.0f, 45.0f, 46.0f,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  43.0f, 44.0f, 47.0f, 48.0f,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  49.0f, 50.0f, 53.0f, 54.0f,</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  51.0f, 52.0f, 55.0f, 56.0f,</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  57.0f, 58.0f, 61.0f, 62.0f,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  59.0f, 60.0f, 63.0f, 64.0f,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  },</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  qScale, qOffset));</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  std::vector<T> outputData(</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  QuantizedVector<T>({</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  10.5f, 14.5f,</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  18.5f, 22.5f,</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  42.5f, 46.5f,</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  50.5f, 54.5f,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  },</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  qScale, qOffset));</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCDHWToNDHWC = { 0, 4, 1, 2, 3 };</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a>)</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCDHWToNDHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  inputData = tmp;</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> </div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  std::vector<T> tmp1(outputData.size());</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCDHWToNDHWC, outputData.data(), tmp1.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  outputData = tmp1;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  }</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  inputData, outputData, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> }</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> LargeTensorsAveragePooling3dTestCommon(</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> {</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 100;</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 5;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 50;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 50;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 50;</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 50;</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> = 50;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> = 50;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 5, 3, 52, 60, 68 }, ArmnnType);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 5, 3, 11, 13, 15 }, ArmnnType);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  {</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  }</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  std::vector<T> input;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(); ++i)</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  {</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  input.push_back(1);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  }</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  std::vector<T> outputExpected;</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>(); ++i)</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  {</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  outputExpected.push_back(1);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  }</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> </div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  input, outputExpected, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span> }</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> </div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> IgnorePaddingSimpleAveragePooling3dTestCommon(</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> {</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 2;</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 2;</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> = 1;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> = 1;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> </div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keyword">auto</span> input = QuantizedVector<T>(</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  {</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  12.0f, 20.0f, 32.0f, 40.0f,</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  24.0f, 40.0f, 64.0f, 80.0f,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  24.0f, 40.0f, 64.0f, 80.0f,</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  24.0f, 40.0f, 64.0f, 80.0f,</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  24.0f, 40.0f, 64.0f, 80.0f,</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  36.0f, 60.0f, 96.0f, 120.0f,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  36.0f, 60.0f, 96.0f, 120.0f,</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  36.0f, 60.0f, 96.0f, 120.0f,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  36.0f, 60.0f, 96.0f, 120.0f,</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  48.0f, 80.0f, 128.0f, 160.0f,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  48.0f, 80.0f, 128.0f, 160.0f,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  48.0f, 80.0f, 128.0f, 160.0f,</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  48.0f, 80.0f, 128.0f, 160.0f,</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  },</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  qScale, qOffset);</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> </div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keyword">auto</span> outputExpected = QuantizedVector<T>(</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  {</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  1.5f, 6.5f, 5.0f,</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  3.0f, 13.0f, 10.0f,</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  1.5f, 6.5f, 5.0f,</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  7.5f, 32.5f, 25.0f,</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  15.0f, 65.0f, 50.0f,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  7.5f, 32.5f, 25.0f,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> </div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  6.0f, 26.0f, 20.0f,</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  12.0f, 52.0f, 40.0f,</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  6.0f, 26.0f, 20.0f,</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  },</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  qScale, qOffset);</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  input, outputExpected, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> }</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span> </div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> SimpleL2Pooling3dTestCommon(</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a>,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> {</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 2;</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 2;</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> </div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 1, 4, 4, 4, dataLayout, ArmnnType);</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(1, 1, 2, 2, 2, dataLayout, ArmnnType);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> </div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  {</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  }</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> </div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  std::vector<T> inputData(</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  QuantizedVector<T>({</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  1.0f, 2.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  3.0f, 4.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  9.0f, 10.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  11.0f, 12.0f, 15.0f, 16.0f,</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  17.0f, 18.0f, 21.0f, 22.0f,</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  19.0f, 20.0f, 23.0f, 24.0f,</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  25.0f, 26.0f, 29.0f, 30.0f,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  27.0f, 28.0f, 31.0f, 32.0f,</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span> </div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  33.0f, 34.0f, 37.0f, 38.0f,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  35.0f, 36.0f, 39.0f, 40.0f,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  41.0f, 42.0f, 45.0f, 46.0f,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  43.0f, 44.0f, 47.0f, 48.0f,</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span> </div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  49.0f, 50.0f, 53.0f, 54.0f,</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  51.0f, 52.0f, 55.0f, 56.0f,</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  57.0f, 58.0f, 61.0f, 62.0f,</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  59.0f, 60.0f, 63.0f, 64.0f,</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  },</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  qScale, qOffset));</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span> </div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  std::vector<T> outputData(</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  QuantizedVector<T>({</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  13.2476412995f, 16.5981926727f,</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  20.1866292382f, 23.9060661758f,</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span> </div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  43.2608367926f, 47.1963981677f,</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  51.1419592898f, 55.0953718564f,</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  },</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  qScale, qOffset));</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span> </div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCDHWToNDHWC = { 0, 4, 1, 2, 3 };</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordflow">if</span> (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a>)</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  {</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCDHWToNDHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  inputData = tmp;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> </div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  std::vector<T> tmp1(outputData.size());</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCDHWToNDHWC, outputData.data(), tmp1.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  outputData = tmp1;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  }</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> </div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  inputData, outputData, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> }</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> </div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> IgnorePaddingSimpleL2Pooling3dTestCommon(</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = 2;</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = 2;</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> = 1;</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> = 1;</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a>;</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> </div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType);</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> </div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  {</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  }</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="keyword">auto</span> input = QuantizedVector<T>(</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  {</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> </div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  2.0f, 3.0f, 4.0f, 5.0f,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> </div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  3.0f, 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  3.0f, 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  3.0f, 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  3.0f, 4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span> </div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  4.0f, 5.0f, 6.0f, 7.0f,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  4.0f, 5.0f, 6.0f, 7.0f,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  4.0f, 5.0f, 6.0f, 7.0f,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  4.0f, 5.0f, 6.0f, 7.0f,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  },</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  qScale, qOffset);</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="keywordtype">float</span> v111 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>(pow(1,2)/8.0f));</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <span class="keywordtype">float</span> v112 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((pow(2,2)+pow(3,2))/8.0f));</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="keywordtype">float</span> v113 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>(pow(4,2)/8));</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span> </div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keywordtype">float</span> v121 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(1,2))/8.0f));</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keywordtype">float</span> v122 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(2,2)+2*pow(3,2))/8.0f));</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keywordtype">float</span> v123 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(4,2))/8.0f));</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> </div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keywordtype">float</span> v131 = v111;</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keywordtype">float</span> v132 = v112;</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <span class="keywordtype">float</span> v133 = v113;</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span> </div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="keywordtype">float</span> v211 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((pow(2,2)+pow(3,2))/8.0f));</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="keywordtype">float</span> v212 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((pow(3,2)+2*pow(4,2)+pow(5,2))/8.0f));</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="keywordtype">float</span> v213 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((pow(5,2)+pow(6,2))/8.0f));</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> </div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="keywordtype">float</span> v221 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(2,2)+2*pow(3,2))/8.0f));</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <span class="keywordtype">float</span> v222 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(3,2)+4*pow(4,2)+2*pow(5,2))/8.0f));</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <span class="keywordtype">float</span> v223 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(5,2)+2*pow(6,2))/8.0f));</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span> </div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="keywordtype">float</span> v231 = v211;</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <span class="keywordtype">float</span> v232 = v212;</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <span class="keywordtype">float</span> v233 = v213;</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span> </div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="keywordtype">float</span> v311 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>(pow(4,2)/8.0f));</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  <span class="keywordtype">float</span> v312 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((pow(5,2)+pow(6,2))/8.0f));</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <span class="keywordtype">float</span> v313 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>(pow(7,2)/8));</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> </div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keywordtype">float</span> v321 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(4,2))/8.0f));</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <span class="keywordtype">float</span> v322 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(5,2)+2*pow(6,2))/8.0f));</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="keywordtype">float</span> v323 = float(<a class="code" href="structarmnn_1_1sqrt.xhtml">sqrt</a>((2*pow(7,2))/8.0f));</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span> </div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <span class="keywordtype">float</span> v331 = v311;</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="keywordtype">float</span> v332 = v312;</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <span class="keywordtype">float</span> v333 = v313;</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span> </div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="keyword">auto</span> outputExpected = QuantizedVector<T>(</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  {</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  v111, v112, v113,</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  v121, v122, v123,</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  v131, v132, v133,</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> </div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  v211, v212, v213,</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  v221, v222, v223,</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  v231, v232, v233,</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> </div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  v311, v312, v313,</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  v321, v322, v323,</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  v331, v332, v333,</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  },</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  qScale, qOffset);</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span> </div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  input, outputExpected, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span> }</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> </div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> AsymmetricNonSquareMaxPooling3dTestCommon(</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> {</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 1, 3, 1 }, ArmnnType);</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 2, 2, 1 }, ArmnnType);</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> </div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a>;</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  descriptor.m_PoolWidth = 1;</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  descriptor.m_PoolHeight = 2;</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  descriptor.m_PoolDepth = 3;</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  descriptor.m_StrideX = 0;</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  descriptor.m_StrideY = 2;</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  descriptor.m_StrideZ = 1;</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  descriptor.m_PadLeft = 0;</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  descriptor.m_PadRight = 0;</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  descriptor.m_PadTop = 2;</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  descriptor.m_PadBottom = 0;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  descriptor.m_PadFront = 1;</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  descriptor.m_PadBack = 2;</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  descriptor.m_OutputShapeRounding = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  descriptor.m_PaddingMethod = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span> </div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <span class="comment">// Construct input data.</span></div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keyword">auto</span> input = QuantizedVector<T>(</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  {</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  },</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  qScale, qOffset);</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span> </div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  <span class="comment">// These were calculated manually.</span></div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="keyword">auto</span> outputExpected = QuantizedVector<T>(</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  {</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  0.0f, 3.0f, 0.0f, 3.0f,</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  },</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  qScale, qOffset);</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span> </div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  input, outputExpected, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> }</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> </div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> AsymmetricNonSquareAveragePooling3dTestCommon(</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> {</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 1, 3, 1 }, ArmnnType);</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 2, 2, 1 }, ArmnnType);</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  descriptor.m_PoolWidth = 1;</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  descriptor.m_PoolHeight = 2;</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  descriptor.m_PoolDepth = 3;</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  descriptor.m_StrideX = 0;</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  descriptor.m_StrideY = 2;</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  descriptor.m_StrideZ = 1;</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  descriptor.m_PadLeft = 0;</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  descriptor.m_PadRight = 0;</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  descriptor.m_PadTop = 2;</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  descriptor.m_PadBottom = 0;</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  descriptor.m_PadFront = 1;</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  descriptor.m_PadBack = 2;</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  descriptor.m_OutputShapeRounding = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  descriptor.m_PaddingMethod = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span> </div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="comment">// Construct input data.</span></div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="keyword">auto</span> input = QuantizedVector<T>(</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  {</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  },</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  qScale, qOffset);</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> </div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <span class="comment">// These were calculated manually.</span></div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <span class="keyword">auto</span> outputExpected = QuantizedVector<T>(</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  {</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  0.0f, 2.0f, 0.0f, 2.0f,</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  },</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  qScale, qOffset);</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span> </div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  input, outputExpected, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span> }</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> </div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> AsymmetricNonSquareL2Pooling3dTestCommon(</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span> {</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 1, 3, 1 }, ArmnnType);</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 2, 2, 1 }, ArmnnType);</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span> </div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a> descriptor;</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  descriptor.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a>;</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  descriptor.m_PoolWidth = 1;</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  descriptor.m_PoolHeight = 2;</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  descriptor.m_PoolDepth = 3;</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  descriptor.m_StrideX = 0;</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  descriptor.m_StrideY = 2;</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  descriptor.m_StrideZ = 1;</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  descriptor.m_PadLeft = 0;</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  descriptor.m_PadRight = 0;</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  descriptor.m_PadTop = 2;</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  descriptor.m_PadBottom = 0;</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  descriptor.m_PadFront = 1;</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  descriptor.m_PadBack = 2;</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  descriptor.m_OutputShapeRounding = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  descriptor.m_PaddingMethod = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span> </div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <span class="comment">// Construct input data.</span></div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  <span class="keyword">auto</span> input = QuantizedVector<T>(</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  {</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  1.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  },</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  qScale, qOffset);</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> </div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="comment">// These were calculated manually.</span></div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  <span class="keyword">auto</span> outputExpected = QuantizedVector<T>(</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  {</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  0.0f, 2.2360679775f, 0.0f, 2.2360679775f,</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  },</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  qScale, qOffset);</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span> </div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="keywordflow">return</span> SimplePooling3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset,</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  input, outputExpected, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span> }</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> </div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> ComparePooling3dTestCommon(</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& refTensorHandleFactory,</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span> {</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 32;</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputDepth = 48;</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelCount = 2;</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 5;</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> </div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> poolSize = 3;</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = 2;</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = 4;</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideZ = 6;</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padX = 0;</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padY = 0;</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padZ = 0;</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span> </div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX;</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY;</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDepth = (inputDepth + 2 * padZ + strideZ - poolSize) / strideZ;</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> </div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span> </div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { batchSize, channelCount, inputHeight, inputWidth, inputDepth };</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { batchSize, channelCount, outputHeight, outputWidth, outputDepth };</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span> </div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(5, inputShape, ArmnnType);</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(5, outputShape, ArmnnType);</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span> </div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  {</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  }</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span> </div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  std::vector<T> input = MakeRandomTensor<T>(inputTensorInfo, 81715);</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  std::vector<T> actualOutput(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  std::vector<T> expectedOutput(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span> </div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 5></a> comparisonResult(outputTensorInfo);</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> </div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span> </div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <a class="code" href="structarmnn_1_1_pooling3d_queue_descriptor.xhtml">armnn::Pooling3dQueueDescriptor</a> data;</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = poolingType;</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = poolSize;</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = poolSize;</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">m_PoolDepth</a> = poolSize;</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">m_StrideZ</a> = strideZ;</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padX;</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padX;</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padY;</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padY;</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">m_PadFront</a> = padZ;</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">m_PadBack</a> = padZ;</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_pooling3d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> </div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refTensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refTensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span> </div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  <span class="comment">// Don't execute if Pooling is not supported, as an exception will be raised.</span></div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  <a class="code" href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a> backend = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>();</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  std::string reasonIfUnsupported;</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  <a class="code" href="classarmnn_1_1_layer_support_handle.xhtml">armnn::LayerSupportHandle</a> handle = <a class="code" href="namespacearmnn.xhtml#a6d9d90e2ace1cd49789835b0073196a3">armnn::GetILayerSupportByBackendId</a>(backend);</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  comparisonResult.m_Supported = handle.<a class="code" href="classarmnn_1_1_layer_support_handle.xhtml#ada2164a21874acb3647cc9224b31a80a">IsPooling3dSupported</a>(inputTensorInfo,</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  outputTensorInfo,</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>,</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  reasonIfUnsupported);</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  <span class="keywordflow">if</span> (!comparisonResult.m_Supported)</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  {</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <span class="keywordflow">return</span> comparisonResult;</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  }</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span> </div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <a class="code" href="structarmnn_1_1_pooling3d_queue_descriptor.xhtml">armnn::Pooling3dQueueDescriptor</a> refData = data;</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> </div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  std::unique_ptr<armnn::IWorkload> workload</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a13060ebb89d2c21a7a5d897e99cccf72">CreateWorkload</a>(<a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c">armnn::LayerType::Pooling3d</a>, data, info);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  std::unique_ptr<armnn::IWorkload> workloadRef</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a13060ebb89d2c21a7a5d897e99cccf72">CreateWorkload</a>(<a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c">armnn::LayerType::Pooling3d</a>, refData, refInfo);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> </div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  outputHandleRef->Allocate();</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  inputHandleRef->Allocate();</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  inputHandle->Allocate();</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  outputHandle->Allocate();</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> </div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  <a class="code" href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), input.data());</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <a class="code" href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), input.data());</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span> </div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  workload->Execute();</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  workloadRef->Execute();</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span> </div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  <a class="code" href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ab5dfed8358e500ed523d78090ec78e88">CopyDataFromITensorHandle</a>(actualOutput.data(), outputHandle.get());</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <a class="code" href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ab5dfed8358e500ed523d78090ec78e88">CopyDataFromITensorHandle</a>(expectedOutput.data(), outputHandleRef.get());</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> </div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  comparisonResult.m_ActualData = actualOutput;</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  comparisonResult.m_ExpectedData = expectedOutput;</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> </div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <span class="keywordflow">return</span> comparisonResult;</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> }</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span> </div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span> </div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> </div><div class="line"><a name="l01068"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a13d8fdbd9b4a36c26f1d06f5702c8e30"> 1068</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a13d8fdbd9b4a36c26f1d06f5702c8e30">SimpleMaxPooling3dSize2x2x2Stride1x1x1Test</a>(</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> {</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  <span class="keywordflow">return</span> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> }</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> </div><div class="line"><a name="l01077"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a145268854a681f2f68b3324a1e73bab3"> 1077</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a145268854a681f2f68b3324a1e73bab3">SimpleMaxPooling3dSize2x2x2Stride1x1x1Uint8Test</a>(</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span> {</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  <span class="keywordflow">return</span> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.1f, 128);</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span> }</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span> </div><div class="line"><a name="l01086"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a5af31c2b40001fabc424ea601e140f1f"> 1086</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a5af31c2b40001fabc424ea601e140f1f">SimpleMaxPooling3dSize2x2x2Stride1x1x1Int16Test</a>(</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> {</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  <span class="keywordflow">return</span> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> }</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> </div><div class="line"><a name="l01095"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a35839cceab373ef3ff90d2061af3d472"> 1095</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a35839cceab373ef3ff90d2061af3d472">SimpleMaxPooling3dTest</a>(</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> {</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <span class="keywordflow">return</span> SimpleMaxPooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> }</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span> </div><div class="line"><a name="l01105"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#ae45322439e8e251cc9064a2bdc5609e7"> 1105</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#ae45322439e8e251cc9064a2bdc5609e7">SimpleMaxPooling3dUint8Test</a>(</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span> {</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  <span class="keywordflow">return</span> SimpleMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> }</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> </div><div class="line"><a name="l01115"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#aeb17ce0d1c35e9191492ad59aeb2e3c5"> 1115</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#aeb17ce0d1c35e9191492ad59aeb2e3c5">SimpleMaxPooling3dInt16Test</a>(</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> {</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keywordflow">return</span> SimpleMaxPooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> }</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span> </div><div class="line"><a name="l01125"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#aeec3874f8a9a726410dbb222be0c9e20"> 1125</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#aeec3874f8a9a726410dbb222be0c9e20">IgnorePaddingSimpleMaxPooling3dTest</a>(</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> {</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span> }</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span> </div><div class="line"><a name="l01134"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#aefe3b6330269050e9273236191c475bf"> 1134</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#aefe3b6330269050e9273236191c475bf">IgnorePaddingSimpleMaxPooling3dUint8Test</a>(</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> {</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5);</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span> }</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span> </div><div class="line"><a name="l01143"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#ad144966cf371b2a0e83e761011f13ace"> 1143</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#ad144966cf371b2a0e83e761011f13ace">IgnorePaddingSimpleMaxPooling3dInt16Test</a>(</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span> {</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span> }</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span> </div><div class="line"><a name="l01152"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a6985e5e677311f83711006df7052e49a"> 1152</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a6985e5e677311f83711006df7052e49a">SimpleAveragePooling3dTest</a>(</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span> {</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="keywordflow">return</span> SimpleAveragePooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> }</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span> </div><div class="line"><a name="l01162"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a10798c546c5a9ce0478acb67df877796"> 1162</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a10798c546c5a9ce0478acb67df877796">SimpleAveragePooling3dUint8Test</a>(</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span> {</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keywordflow">return</span> SimpleAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span> }</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> </div><div class="line"><a name="l01172"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a794276f7cda68b1f300281ad9666aa2c"> 1172</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a794276f7cda68b1f300281ad9666aa2c">SimpleAveragePooling3dInt16Test</a>(</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> {</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <span class="keywordflow">return</span> SimpleAveragePooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span> }</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span> </div><div class="line"><a name="l01182"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a8e9b5f9f151897fac1856acfe4aaef7a"> 1182</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a8e9b5f9f151897fac1856acfe4aaef7a">SimpleL2Pooling3dTest</a>(</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span> {</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <span class="keywordflow">return</span> SimpleL2Pooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span> }</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> </div><div class="line"><a name="l01192"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#ab2af2bcea5a7d43add9e0c74d2c474ef"> 1192</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#ab2af2bcea5a7d43add9e0c74d2c474ef">SimpleL2Pooling3dUint8Test</a>(</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span> {</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <span class="keywordflow">return</span> SimpleL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> }</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> </div><div class="line"><a name="l01202"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#ad8e9d660c043c7bb96f51f8e83affcdf"> 1202</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#ad8e9d660c043c7bb96f51f8e83affcdf">SimpleL2Pooling3dInt16Test</a>(</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span> {</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  <span class="keywordflow">return</span> SimpleL2Pooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  workloadFactory, memoryManager, tensorHandleFactory, dataLayout);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span> }</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> </div><div class="line"><a name="l01212"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a1be3de96f029631465553441dd01e723"> 1212</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a1be3de96f029631465553441dd01e723">LargeTensorsAveragePooling3dTest</a>(</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span> {</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <span class="keywordflow">return</span> LargeTensorsAveragePooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span> }</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> </div><div class="line"><a name="l01221"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a249834da6eff5fcf8dcc0347b696e736"> 1221</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a249834da6eff5fcf8dcc0347b696e736">LargeTensorsAveragePooling3dUint8Test</a>(</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span> {</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  <span class="keywordflow">return</span> LargeTensorsAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5, -1);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span> }</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span> </div><div class="line"><a name="l01230"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a83a375ec3a0c4492cf829ce3aeafd330"> 1230</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a83a375ec3a0c4492cf829ce3aeafd330">LargeTensorsAveragePooling3dInt16Test</a>(</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span> {</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <span class="keywordflow">return</span> LargeTensorsAveragePooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span> }</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> </div><div class="line"><a name="l01239"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a4d1572602310835bee059328c2455dc8"> 1239</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a4d1572602310835bee059328c2455dc8">IgnorePaddingSimpleAveragePooling3dTest</a>(</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span> {</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span> }</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span> </div><div class="line"><a name="l01248"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a1e74ad9085ed79e8015b2ca6df2ce6b4"> 1248</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a1e74ad9085ed79e8015b2ca6df2ce6b4">IgnorePaddingSimpleAveragePooling3dUint8Test</a>(</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> {</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> }</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span> </div><div class="line"><a name="l01257"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#af455e67fbc9bc19199d3a88209685df9"> 1257</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#af455e67fbc9bc19199d3a88209685df9">IgnorePaddingSimpleAveragePooling3dInt16Test</a>(</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> {</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span> }</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span> </div><div class="line"><a name="l01266"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#ae4a30844a77c54dc5e1d489a013fd3c4"> 1266</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#ae4a30844a77c54dc5e1d489a013fd3c4">IgnorePaddingSimpleL2Pooling3dTest</a>(</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span> {</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span> }</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span> </div><div class="line"><a name="l01275"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a19705339ac39ce056d62740ade30ec5e"> 1275</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a19705339ac39ce056d62740ade30ec5e">IgnorePaddingSimpleL2Pooling3dUint8Test</a>(</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span> {</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5);</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span> }</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span> </div><div class="line"><a name="l01284"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a6ff7fa315a8a5a4e78ed719e7ab40a60"> 1284</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a6ff7fa315a8a5a4e78ed719e7ab40a60">IgnorePaddingSimpleL2Pooling3dInt16Test</a>(</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span> {</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <span class="keywordflow">return</span> IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span> }</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span> </div><div class="line"><a name="l01293"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a7e660e9f06a2f30e849f491c01e803bb"> 1293</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a7e660e9f06a2f30e849f491c01e803bb">AsymmetricNonSquareMaxPooling3dTest</a>(</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span> {</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <span class="keywordflow">return</span> AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span> }</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span> </div><div class="line"><a name="l01302"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a61e7169af03dc796af267084411282e5"> 1302</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a61e7169af03dc796af267084411282e5">AsymmetricNonSquareMaxPooling3dUint8Test</a>(</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span> {</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <span class="keywordflow">return</span> AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span> }</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span> </div><div class="line"><a name="l01311"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a8fbf140825461f5bffdc380ef5736961"> 1311</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a8fbf140825461f5bffdc380ef5736961">AsymmetricNonSquareMaxPooling3dInt16Test</a>(</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> {</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="keywordflow">return</span> AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span> }</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span> </div><div class="line"><a name="l01320"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a80640533d9eca4dcbcf5da3a10deb860"> 1320</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a80640533d9eca4dcbcf5da3a10deb860">AsymmetricNonSquareAveragePooling3dTest</a>(</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span> {</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <span class="keywordflow">return</span> AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span> }</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span> </div><div class="line"><a name="l01329"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a26d1e347a12bd20576697f628f3e6002"> 1329</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a26d1e347a12bd20576697f628f3e6002">AsymmetricNonSquareAveragePooling3dUint8Test</a>(</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span> {</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  <span class="keywordflow">return</span> AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span> }</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span> </div><div class="line"><a name="l01338"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a069e6e23f81bf4ae5212cac843676d96"> 1338</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a069e6e23f81bf4ae5212cac843676d96">AsymmetricNonSquareAveragePooling3dInt16Test</a>(</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> {</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <span class="keywordflow">return</span> AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span> }</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> </div><div class="line"><a name="l01347"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a5ff698a3de75562784a465de4d165e94"> 1347</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a5ff698a3de75562784a465de4d165e94">AsymmetricNonSquareL2Pooling3dTest</a>(</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span> {</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  <span class="keywordflow">return</span> AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span> }</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span> </div><div class="line"><a name="l01356"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a8ecc314fb10e4939696e1916c6810bb3"> 1356</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a8ecc314fb10e4939696e1916c6810bb3">AsymmetricNonSquareL2Pooling3dUint8Test</a>(</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span> {</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  <span class="keywordflow">return</span> AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span> }</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span> </div><div class="line"><a name="l01365"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#af917767bb3a96f3116dfe9fcec244cbd"> 1365</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#af917767bb3a96f3116dfe9fcec244cbd">AsymmetricNonSquareL2Pooling3dInt16Test</a>(</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span> {</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <span class="keywordflow">return</span> AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  workloadFactory, memoryManager, tensorHandleFactory);</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span> }</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span> </div><div class="line"><a name="l01374"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a1f71c5a019e0ac7ee51e094b3e009a80"> 1374</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a1f71c5a019e0ac7ee51e094b3e009a80">ComparePooling3dTest</a>(</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& refTensorHandleFactory,</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType)</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span> {</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <span class="keywordflow">return</span> ComparePooling3dTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, poolingType);</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span> }</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span> </div><div class="line"><a name="l01386"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a1b2ec0ddfdb5009eedbc7f44b088c5f0"> 1386</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a1b2ec0ddfdb5009eedbc7f44b088c5f0">ComparePooling3dUint8Test</a>(</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& refTensorHandleFactory,</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType)</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span> {</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <span class="keywordflow">return</span> ComparePooling3dTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory,</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  poolingType, 0.1f, 128);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span> }</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span> </div><div class="line"><a name="l01399"></a><span class="lineno"><a class="line" href="_pooling3d_test_impl_8hpp.xhtml#a4d3fe9dda73c023008f857a515d874e7"> 1399</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 5></a> <a class="code" href="_pooling3d_test_impl_8cpp.xhtml#a4d3fe9dda73c023008f857a515d874e7">ComparePooling3dInt16Test</a>(</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& refTensorHandleFactory,</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a> poolingType)</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span> {</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  <span class="keywordflow">return</span> ComparePooling3dTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, poolingType);</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span> }</div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorShape::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdoc">Function that calculates the tensor elements by multiplying all dimension size which are Specified...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00181">Tensor.cpp:181</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling3dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00434">Descriptors.hpp:434</a></div></div> +<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a9f7e4296485d2812e7996089149c96d1"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">armnn::IWorkloadFactory::GetBackendId</a></div><div class="ttdeci">virtual const BackendId & GetBackendId() const =0</div></div> +<div class="ttc" id="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a61e7169af03dc796af267084411282e5"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a61e7169af03dc796af267084411282e5">AsymmetricNonSquareMaxPooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > AsymmetricNonSquareMaxPooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01302">Pooling3dTestImpl.cpp:1302</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00049">Types.hpp:49</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00025">DataLayoutIndexed.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling3dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00456">Descriptors.hpp:456</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00191">Tensor.hpp:191</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling3dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00448">Descriptors.hpp:448</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_af917767bb3a96f3116dfe9fcec244cbd"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#af917767bb3a96f3116dfe9fcec244cbd">AsymmetricNonSquareL2Pooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > AsymmetricNonSquareL2Pooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01365">Pooling3dTestImpl.cpp:1365</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_acc978b36fd5d949bc781d7638e6e08b9"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#acc978b36fd5d949bc781d7638e6e08b9">armnn::Pooling3dDescriptor::m_PoolDepth</a></div><div class="ttdeci">uint32_t m_PoolDepth</div><div class="ttdoc">Pooling depth value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00452">Descriptors.hpp:452</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a794276f7cda68b1f300281ad9666aa2c"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a794276f7cda68b1f300281ad9666aa2c">SimpleAveragePooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > SimpleAveragePooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01172">Pooling3dTestImpl.cpp:1172</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3"><div class="ttname"><a href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_factory_8hpp_source.xhtml#l00022">WorkloadFactory.hpp:22</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a6ff7fa315a8a5a4e78ed719e7ab40a60"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a6ff7fa315a8a5a4e78ed719e7ab40a60">IgnorePaddingSimpleL2Pooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > IgnorePaddingSimpleL2Pooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01284">Pooling3dTestImpl.cpp:1284</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a7e660e9f06a2f30e849f491c01e803bb"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a7e660e9f06a2f30e849f491c01e803bb">AsymmetricNonSquareMaxPooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > AsymmetricNonSquareMaxPooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01293">Pooling3dTestImpl.cpp:1293</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a069e6e23f81bf4ae5212cac843676d96"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a069e6e23f81bf4ae5212cac843676d96">AsymmetricNonSquareAveragePooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > AsymmetricNonSquareAveragePooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01338">Pooling3dTestImpl.cpp:1338</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a4d3fe9dda73c023008f857a515d874e7"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a4d3fe9dda73c023008f857a515d874e7">ComparePooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > ComparePooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::ITensorHandleFactory &refTensorHandleFactory, armnn::PoolingAlgorithm poolingType)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01399">Pooling3dTestImpl.cpp:1399</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&#39;t count and are ignored. </div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a5af31c2b40001fabc424ea601e140f1f"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a5af31c2b40001fabc424ea601e140f1f">SimpleMaxPooling3dSize2x2x2Stride1x1x1Int16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > SimpleMaxPooling3dSize2x2x2Stride1x1x1Int16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01086">Pooling3dTestImpl.cpp:1086</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_support_handle_xhtml_ada2164a21874acb3647cc9224b31a80a"><div class="ttname"><a href="classarmnn_1_1_layer_support_handle.xhtml#ada2164a21874acb3647cc9224b31a80a">armnn::LayerSupportHandle::IsPooling3dSupported</a></div><div class="ttdeci">bool IsPooling3dSupported(const TensorInfo &input, const TensorInfo &output, const Pooling3dDescriptor &descriptor, Optional< std::string &> reasonIfUnsupported=EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_backend_helper_8cpp_source.xhtml#l00945">BackendHelper.cpp:945</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_aeec3874f8a9a726410dbb222be0c9e20"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#aeec3874f8a9a726410dbb222be0c9e20">IgnorePaddingSimpleMaxPooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > IgnorePaddingSimpleMaxPooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01125">Pooling3dTestImpl.cpp:1125</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling3dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a45687cf74c4aeeacc6605a1f7ffe582a"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a45687cf74c4aeeacc6605a1f7ffe582a">armnnUtils::DataLayoutIndexed::GetDepthIndex</a></div><div class="ttdeci">unsigned int GetDepthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00026">DataLayoutIndexed.hpp:26</a></div></div> +<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a1f71c5a019e0ac7ee51e094b3e009a80"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a1f71c5a019e0ac7ee51e094b3e009a80">ComparePooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > ComparePooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::ITensorHandleFactory &refTensorHandleFactory, armnn::PoolingAlgorithm poolingType)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01374">Pooling3dTestImpl.cpp:1374</a></div></div> +<div class="ttc" id="_backend_helper_8hpp_xhtml"><div class="ttname"><a href="_backend_helper_8hpp.xhtml">BackendHelper.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling3dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCDHW, NDHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00464">Descriptors.hpp:464</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a145268854a681f2f68b3324a1e73bab3"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a145268854a681f2f68b3324a1e73bab3">SimpleMaxPooling3dSize2x2x2Stride1x1x1Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > SimpleMaxPooling3dSize2x2x2Stride1x1x1Uint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01077">Pooling3dTestImpl.cpp:1077</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8hpp_xhtml"><div class="ttname"><a href="_pooling3d_test_impl_8hpp.xhtml">Pooling3dTestImpl.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a83ca447892f460dabaa2f87d3dc3db61"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a83ca447892f460dabaa2f87d3dc3db61">armnn::Pooling3dDescriptor::m_PadFront</a></div><div class="ttdeci">uint32_t m_PadFront</div><div class="ttdoc">Padding front value in the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00444">Descriptors.hpp:444</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a></div><div class="ttdeci">PoolingAlgorithm</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00123">Types.hpp:123</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling3d_queue_descriptor.xhtml">armnn::Pooling3dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00195">WorkloadData.hpp:195</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling3dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00450">Descriptors.hpp:450</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00054">WorkloadData.hpp:54</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a249834da6eff5fcf8dcc0347b696e736"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a249834da6eff5fcf8dcc0347b696e736">LargeTensorsAveragePooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > LargeTensorsAveragePooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01221">Pooling3dTestImpl.cpp:1221</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00024">DataLayoutIndexed.hpp:24</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a19705339ac39ce056d62740ade30ec5e"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a19705339ac39ce056d62740ade30ec5e">IgnorePaddingSimpleL2Pooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > IgnorePaddingSimpleL2Pooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01275">Pooling3dTestImpl.cpp:1275</a></div></div> +<div class="ttc" id="_numeric_cast_8hpp_xhtml"><div class="ttname"><a href="_numeric_cast_8hpp.xhtml">NumericCast.hpp</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a80640533d9eca4dcbcf5da3a10deb860"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a80640533d9eca4dcbcf5da3a10deb860">AsymmetricNonSquareAveragePooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > AsymmetricNonSquareAveragePooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01320">Pooling3dTestImpl.cpp:1320</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00131">Permute.cpp:131</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_ad144966cf371b2a0e83e761011f13ace"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#ad144966cf371b2a0e83e761011f13ace">IgnorePaddingSimpleMaxPooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > IgnorePaddingSimpleMaxPooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01143">Pooling3dTestImpl.cpp:1143</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a884e0167ebf9bbe6cfd6ca5ab97ab015">armnn::DataLayout::NCDHW</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a11d5c25face9b54e90f79ee8bdc1d0fb"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a11d5c25face9b54e90f79ee8bdc1d0fb">armnn::Pooling3dDescriptor::m_PadBack</a></div><div class="ttdeci">uint32_t m_PadBack</div><div class="ttdoc">Padding back value in the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00446">Descriptors.hpp:446</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a8e9b5f9f151897fac1856acfe4aaef7a"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a8e9b5f9f151897fac1856acfe4aaef7a">SimpleL2Pooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > SimpleL2Pooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01182">Pooling3dTestImpl.cpp:1182</a></div></div> +<div class="ttc" id="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a1e74ad9085ed79e8015b2ca6df2ce6b4"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a1e74ad9085ed79e8015b2ca6df2ce6b4">IgnorePaddingSimpleAveragePooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > IgnorePaddingSimpleAveragePooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01248">Pooling3dTestImpl.cpp:1248</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_backend_internal_8hpp_source.xhtml#l00096">IBackendInternal.hpp:96</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a13d8fdbd9b4a36c26f1d06f5702c8e30"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a13d8fdbd9b4a36c26f1d06f5702c8e30">SimpleMaxPooling3dSize2x2x2Stride1x1x1Test</a></div><div class="ttdeci">LayerTestResult< float, 5 > SimpleMaxPooling3dSize2x2x2Stride1x1x1Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01068">Pooling3dTestImpl.cpp:1068</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">armnn::Pooling3dDescriptor::m_OutputShapeRounding</a></div><div class="ttdeci">OutputShapeRounding m_OutputShapeRounding</div><div class="ttdoc">The rounding method for the output shape. (Floor, Ceiling). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00460">Descriptors.hpp:460</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling3dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00442">Descriptors.hpp:442</a></div></div> +<div class="ttc" id="include_2armnn_test_utils_2_tensor_copy_utils_8hpp_xhtml_ab5dfed8358e500ed523d78090ec78e88"><div class="ttname"><a href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ab5dfed8358e500ed523d78090ec78e88">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *mem, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00475">Tensor.cpp:475</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a26d1e347a12bd20576697f628f3e6002"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a26d1e347a12bd20576697f628f3e6002">AsymmetricNonSquareAveragePooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > AsymmetricNonSquareAveragePooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01329">Pooling3dTestImpl.cpp:1329</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2b3140dc366b9fcd25ed786a79d1817c">armnn::LayerType::Pooling3d</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_ae4a30844a77c54dc5e1d489a013fd3c4"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#ae4a30844a77c54dc5e1d489a013fd3c4">IgnorePaddingSimpleL2Pooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > IgnorePaddingSimpleL2Pooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01266">Pooling3dTestImpl.cpp:1266</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00282">Types.hpp:282</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a5ff698a3de75562784a465de4d165e94"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a5ff698a3de75562784a465de4d165e94">AsymmetricNonSquareL2Pooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > AsymmetricNonSquareL2Pooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01347">Pooling3dTestImpl.cpp:1347</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a6985e5e677311f83711006df7052e49a"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a6985e5e677311f83711006df7052e49a">SimpleAveragePooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > SimpleAveragePooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01152">Pooling3dTestImpl.cpp:1152</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_aefe3b6330269050e9273236191c475bf"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#aefe3b6330269050e9273236191c475bf">IgnorePaddingSimpleMaxPooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > IgnorePaddingSimpleMaxPooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01134">Pooling3dTestImpl.cpp:1134</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_ae45322439e8e251cc9064a2bdc5609e7"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#ae45322439e8e251cc9064a2bdc5609e7">SimpleMaxPooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > SimpleMaxPooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01105">Pooling3dTestImpl.cpp:1105</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_support_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_support_handle.xhtml">armnn::LayerSupportHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_helper_8hpp_source.xhtml#l00020">BackendHelper.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml">armnn::Pooling3dDescriptor</a></div><div class="ttdoc">A Pooling3dDescriptor for the Pooling3dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a5164336f6a1b15be0d434a6bbf7289da"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a5164336f6a1b15be0d434a6bbf7289da">armnn::Pooling3dDescriptor::m_StrideZ</a></div><div class="ttdeci">uint32_t m_StrideZ</div><div class="ttdoc">Stride value when proceeding through input for the depth dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00458">Descriptors.hpp:458</a></div></div> +<div class="ttc" id="include_2armnn_test_utils_2_tensor_helpers_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_test_utils_2_tensor_helpers_8hpp.xhtml">TensorHelpers.hpp</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_aeb17ce0d1c35e9191492ad59aeb2e3c5"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#aeb17ce0d1c35e9191492ad59aeb2e3c5">SimpleMaxPooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > SimpleMaxPooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01115">Pooling3dTestImpl.cpp:1115</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a1b2ec0ddfdb5009eedbc7f44b088c5f0"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a1b2ec0ddfdb5009eedbc7f44b088c5f0">ComparePooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > ComparePooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::ITensorHandleFactory &refTensorHandleFactory, armnn::PoolingAlgorithm poolingType)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01386">Pooling3dTestImpl.cpp:1386</a></div></div> +<div class="ttc" id="_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_utils_8hpp.xhtml">TensorUtils.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling3dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00436">Descriptors.hpp:436</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2faad301514192636ad34210adce598a45a">armnn::PaddingMethod::IgnoreValue</a></div><div class="ttdoc">The padding fields count, but are ignored. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> +<div class="ttc" id="include_2armnn_test_utils_2_tensor_copy_utils_8hpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a1be3de96f029631465553441dd01e723"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a1be3de96f029631465553441dd01e723">LargeTensorsAveragePooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > LargeTensorsAveragePooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01212">Pooling3dTestImpl.cpp:1212</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_factory_8hpp_source.xhtml#l00042">ITensorHandleFactory.hpp:42</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling3dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div> +<div class="ttc" id="src_2backends_2backends_common_2_workload_info_8hpp_xhtml"><div class="ttname"><a href="src_2backends_2backends_common_2_workload_info_8hpp.xhtml">WorkloadInfo.hpp</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_af455e67fbc9bc19199d3a88209685df9"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#af455e67fbc9bc19199d3a88209685df9">IgnorePaddingSimpleAveragePooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > IgnorePaddingSimpleAveragePooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01257">Pooling3dTestImpl.cpp:1257</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a83a375ec3a0c4492cf829ce3aeafd330"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a83a375ec3a0c4492cf829ce3aeafd330">LargeTensorsAveragePooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > LargeTensorsAveragePooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01230">Pooling3dTestImpl.cpp:1230</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_ad8e9d660c043c7bb96f51f8e83affcdf"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#ad8e9d660c043c7bb96f51f8e83affcdf">SimpleL2Pooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > SimpleL2Pooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01202">Pooling3dTestImpl.cpp:1202</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a4d1572602310835bee059328c2455dc8"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a4d1572602310835bee059328c2455dc8">IgnorePaddingSimpleAveragePooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > IgnorePaddingSimpleAveragePooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01239">Pooling3dTestImpl.cpp:1239</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a375ca3cff9f1b005d1412dc5f3cf5b6e"><div class="ttname"><a href="namespacearmnn.xhtml#a375ca3cff9f1b005d1412dc5f3cf5b6e">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00035">NumericCast.hpp:35</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_utils_2_compatible_types_8hpp_source.xhtml#l00010">CompatibleTypes.hpp:10</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_ab2af2bcea5a7d43add9e0c74d2c474ef"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#ab2af2bcea5a7d43add9e0c74d2c474ef">SimpleL2Pooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > SimpleL2Pooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01192">Pooling3dTestImpl.cpp:1192</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a6a061313d22e51e0f25b7cd4dc065233">armnn::PoolingAlgorithm::Max</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718a7e6aa2d53f6ee2b1a34b017fa403cb76">armnn::PoolingAlgorithm::L2</a></div></div> +<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_test_utils_2_layer_test_result_8hpp_source.xhtml#l00015">LayerTestResult.hpp:15</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00491">Tensor.cpp:491</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a35839cceab373ef3ff90d2061af3d472"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a35839cceab373ef3ff90d2061af3d472">SimpleMaxPooling3dTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > SimpleMaxPooling3dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01095">Pooling3dTestImpl.cpp:1095</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a6d9d90e2ace1cd49789835b0073196a3"><div class="ttname"><a href="namespacearmnn.xhtml#a6d9d90e2ace1cd49789835b0073196a3">armnn::GetILayerSupportByBackendId</a></div><div class="ttdeci">LayerSupportHandle GetILayerSupportByBackendId(const armnn::BackendId &backend)</div><div class="ttdoc">Convenience function to retrieve the ILayerSupportHandle for a backend. </div><div class="ttdef"><b>Definition:</b> <a href="_backend_helper_8cpp_source.xhtml#l00016">BackendHelper.cpp:16</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling3dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00454">Descriptors.hpp:454</a></div></div> +<div class="ttc" id="structarmnn_1_1sqrt_xhtml"><div class="ttname"><a href="structarmnn_1_1sqrt.xhtml">armnn::sqrt</a></div><div class="ttdef"><b>Definition:</b> <a href="_sqrt_8hpp_source.xhtml#l00013">Sqrt.hpp:13</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a10798c546c5a9ce0478acb67df877796"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a10798c546c5a9ce0478acb67df877796">SimpleAveragePooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > SimpleAveragePooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01162">Pooling3dTestImpl.cpp:1162</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a8ecc314fb10e4939696e1916c6810bb3"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a8ecc314fb10e4939696e1916c6810bb3">AsymmetricNonSquareL2Pooling3dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 5 > AsymmetricNonSquareL2Pooling3dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01356">Pooling3dTestImpl.cpp:1356</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling3d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling3d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling3dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00462">Descriptors.hpp:462</a></div></div> +<div class="ttc" id="_pooling3d_test_impl_8cpp_xhtml_a8fbf140825461f5bffdc380ef5736961"><div class="ttname"><a href="_pooling3d_test_impl_8cpp.xhtml#a8fbf140825461f5bffdc380ef5736961">AsymmetricNonSquareMaxPooling3dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 5 > AsymmetricNonSquareMaxPooling3dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_pooling3d_test_impl_8cpp_source.xhtml#l01311">Pooling3dTestImpl.cpp:1311</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a13060ebb89d2c21a7a5d897e99cccf72"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a13060ebb89d2c21a7a5d897e99cccf72">armnn::IWorkloadFactory::CreateWorkload</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateWorkload(LayerType type, const QueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01541">WorkloadFactory.cpp:1541</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a4dd0194b114cbf51da5b3a72569863ef">armnn::DataLayout::NDHWC</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">armnnUtils::DataLayoutIndexed::GetChannelsIndex</a></div><div class="ttdeci">unsigned int GetChannelsIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00023">DataLayoutIndexed.hpp:23</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_a375f11dd42ff042435e8771cf287b20c"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">armnn::ITensorHandleFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0</div></div> +<div class="ttc" id="include_2armnn_test_utils_2_tensor_copy_utils_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_test_utils_2_tensor_copy_utils_8hpp.xhtml">TensorCopyUtils.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00196">Tensor.hpp:196</a></div></div> +<div class="ttc" id="classarmnn_1_1_backend_id_xhtml"><div class="ttname"><a href="classarmnn_1_1_backend_id.xhtml">armnn::BackendId</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00075">BackendId.hpp:75</a></div></div> +<div class="ttc" id="include_2armnn_test_utils_2_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_test_utils_2_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</a></div></div> +</div><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_797a213d7d01b98ef12d53b0820ea64e.xhtml">backendsCommon</a></li><li class="navelem"><a class="el" href="dir_28bfe507f7e135bdae07c2a6b7f66696.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_99a30439342d160875b21dac3498ad7f.xhtml">layerTests</a></li><li class="navelem"><a class="el" href="_pooling3d_test_impl_8cpp.xhtml">Pooling3dTestImpl.cpp</a></li> + <li class="footer">Generated on Wed Mar 9 2022 12:00:57 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |